Air quality characteristics during 2016–2020 in Wuhan, China

Implementation of a clean air policy in China has high national importance. Here, we analyzed tempo-spatial characteristics of the concentrations of PM2.5 (PM2.5_C), PM10 (PM10_C), SO2 (SO2 _C), NO2 (NO2 _C), CO (CO _C), and maximum 8-h average O3 (O3_8h_C), monitored at 22 stations throughout the mega-city of Wuhan from January 2016 to December 2020, and their correlations with the meteorological and socio-economic factors. PM2.5_C, PM10_C, SO2 _C, NO2 _C, and CO _C showed similar monthly and seasonal trends, with minimum value in summer and maximum value in winter. However, O3_8h_C showed an opposite monthly and seasonal change pattern. In 2020, compared to the other years, the annual average PM2.5_C, PM10_C, SO2 _C, NO2 _C, and CO _C were lower. PM2.5_C and PM10_C were higher in urban and industrial sites and lower in the control site. The SO2_C was higher in industrial sites. The NO2_C was lower, and O3_8h_C was higher in suburban sites, while CO showed no spatial differences in their concentrations. PM2.5 _C, PM10 _C, SO2 _C, NO2 _C, and CO _C had positive correlations with each other, while O3_8h_C showed more complex correlations with the other pollutants. PM2.5_C, PM10_C, SO2 _C, and CO _C presented a significantly negative association with temperature and precipitation, while O3 was significantly positively associated with temperature and negatively associated with relative air humidity. There was no significant correlation between air pollutants and wind speed. Gross domestic product, population, number of automobiles, and energy consumption play an important role in the dynamics of air quality concentrations. These all provided important information for the decision and policy-makers to effectively control the air pollution in Wuhan.

In past decades, the deterioration of air quality caused by increased human activity and manufacturing has attracted wide concern worldwide 1,2 . This not only reduces the visibility of the air atmosphere 3 but also significantly harms human health 4 and endangers the sustainable development of society and the economy 5 . High concentrations of PM 2.5 and PM 10 would reduce atmospheric visibility and increase the occurrence of traffic accidents, while excessive exposure to polluted air could cause many kinds of cardiovascular and chronic respiratory diseases (e.g. asthma) 6 or even lead to premature death and cancer 2 . High concentrations of SO 2 and NO x could cause acid rain and bring serious adverse ecosystem effects, such as the corrosion of buildings, soil acidification, and damage to crops and the aquatic environment 7,8 . Therefore, it is urgent to reduce air pollutants and improve air quality.
Urban air quality is influenced by various factors, including socio-economic factors (e.g., the level of economic development, urban population, car ownership, fuel emission, and usage of fossil resources, etc.) and meteorological factors (e.g., temperature, relative air humidity, wind speed, and precipitation, etc.) 9,10 . Socioeconomic factors are the main pollution sources affecting urban air quality 11,12 , while meteorological conditions also influence air quality when the main pollution sources are relatively stable [13][14][15][16] . For example, urban emissions from human activity and manufacturing could cause environmental problems, such as photochemical smog, ozone layer depletion, acid rain, toxic chemical pollution, and global climate warming 17,18 . Monthly or seasonal air quality variations are caused by pollutant-intensive emission sources and meteorological conditions 13,19,20 . Therefore, strict and effective regulations and measures for air pollution prevention have begun to be implemented worldwide 21,22 .
China has suffered serious environmental degradation and air pollution, accompanying rapid economic growth and urbanization in recent decades. Population growth, energy consumption, motor vehicle increment, www.nature.com/scientificreports/ and industrial dust emission have become China's main causes of air pollution 23 . Many studies have reported that air pollution strongly impacts people's health and life 24,25 . In order to mitigate air pollution, many pollution reduction technologies and policies have been implemented to improve air quality. The control measures (e.g., renewable energy utilization, traffic control, and flue gas desulfurization and denitration) adopted by China's central and local governments have achieved remarkable results. However, air pollution is still very serious 26,27 , and has gradually become a hot topic of high concern in China 28 . Scientific evaluation of the temporal change and spatial differences in air quality characteristics can help the government to examine the air pollution status and dynamics, while systematic analysis of the factors affecting air quality could provide evidence for policymakers to formulate effective measures 10 , and to optimize the urban expansion and development pattern, and land use/ land cover characteristics to improve air quality at city levels [29][30][31][32] . The temporal characteristics of air quality and their driving factors have been analyzed across multiple spatial scales in China, mainly in megacities (e.g., Beijing, Shanghai, Guangzhou, and Shenzhen, etc.) 23,[33][34][35] . However, the spatial heterogeneity in air pollutants has seldom been evaluated at city scales due to limited observation sites. As one of the important and core cities in China's Yangtze River Economic Belt, Wuhan's rapid industrialization and urbanization have achieved short-term gains at the expense of the environment. Assessing and exploring air quality in Wuhan will guide Hubei Province and even the Yangtze River Economic Belt to a certain extent. However, few studies have comprehensively examined the temporal characteristics of air quality in Wuhan city, especially the spatial variations characteristics.
In this study, the official data on daily Individual Air Quality Index (IAQI) of PM 2.5 , PM 10 , SO 2 , NO 2 , CO, and maximum 8-h average O 3 (O 3 _8h), monitored at 22 stations throughout the mega-city of Wuhan from January 2016 to December 2020, were used to examine spatio-temporal characteristics of air pollution in Wuhan city. The main objectives of this study are: (1) evaluate the variations in the concentrations of PM 2.5 (PM 2.5 _C), PM 10 (PM 10 _C), SO 2 (SO 2 _C), NO 2 (NO 2 _C), CO (CO _C), and O 3 _8h (O 3 _8h_C) at monthly, seasonal, and yearly scales in the air in Wuhan city during 2016-2020; (2) examine the spatial differences in PM 2.5 _C, PM 10 _C, SO 2 _C, NO 2 _C, CO_C, and O 3 _8h_C at seasonal and yearly scales across different sites; and (3) analyze the influence of the main meteorological factors and socio-economic indicators on air pollutants in Wuhan city. The results should provide city-scale evidence on spatio-temporal characteristics of air pollutants and a scientific basis for taking effective measures and new policy proposals to improve air quality in Wuhan city.

Materials and methods
Study area. Wuhan has a subtropical monsoon, humid climate with four distinct seasons. The annual average precipitation is 1205 mm, and the annual average temperature is 15.8-17.5°C 36 . As one of central China's most important cities and mega-cities, Wuhan has a land area of 8569.15 km 2 . It is an important hub in China due to its many waterways, convenient transportation, and advantageous position. With the rapid rise in its scale, the central strategic fulcrum of Wuhan city has become increasingly significant. Rapid urbanization, high population growth, a large number of vehicles, and high energy consumption have all led to ecological degradation in recent decades in Wuhan 37 .
Sampling site and air quality datasets. Data on the daily IAQI of PM 2.5 , PM 10

Meteorological and socio-economic datasets.
Daily meteorological data of the Wuhan meteorological observation station (30°37' N, 114°08' E) were downloaded from the China Meteorological Data Sharing Service System (http:// data. cma. cn/). The meteorological factors data mainly include air temperature (T, °C), relative air humidity (RH, %), precipitation (Prec, mm), and wind speed (w, m/s) from 2016 to 2020. The data on socio-economic factors were obtained from the Wuhan Statistical Yearbook (http:// tjj. wuhan. gov. cn/). The socio-economic factors data mainly include the yearly gross domestic product (GDP, 100 million yuan), per capita regional GDP (PGDP, yuan/population), permanent resident population (PRP, ten thousand population), population density (PD, population/km 2 ), road area (RA, × 10 4 m 2 ), number of civilian vehicles (CV), per capita area of parks and green spaces (PPGA, m 2 ), the green coverage rate of built-up areas (GRB, %), total energy combustion (EC, tons of standard coal/km 2 ), coal combustion (CAC , tons of standard coal/km 2 ), coke combustion (CKC, tons of standard coal/km 2 ), crude oil combustion (COC, tons of standard coal/km 2 ), fuel oil combustion (FOC, tons of standard coal/km 2 ), and electric power combustion (EPC, kWh) from 2009 to 2020. The data from 2020 were excluded from the correlation analysis results due to COVID-19 in 2020. www.nature.com/scientificreports/ yearly data were mainly used to analyze the correlation between air quality, meteorological, and socio-economic factors. Before correlation analysis, independence, and normality tests were conducted on the data. Pearson correlation coefficients were calculated for the relationships between air pollutants and the meteorological and socio-economic data. The correlation between factors was regarded as statistically significant or highly significant when the P value was less than 0.05 or 0.01, respectively. Detailed methods can be referred to Chen et al. 23

Results
Temporal variations in air pollutants. PM 2.5 _C, PM 10 _C, SO 2 _C, NO 2 _C, and CO_C showed similar monthly and seasonal trends. In contrast, O 3 _8h_C showed opposite monthly and seasonal trends (Fig. 2). At the monthly scale, PM 2.5 _C, PM 10 _C, SO 2 _C, NO 2 _C, and CO_C all showed a single-valley change pattern, with the minimum value in July and the maximum value in December or January. In comparison, O 3 _8h_C showed a double-peak change pattern, with the peak value in June and September (Fig. 2a). At the seasonal scale, the maximum values of PM 2.5 _C, PM 10 _C, SO 2 _C, NO 2 _C, and CO_C were in winter (December, January, and February) and the minimum values were in summer (June, July, and August), while the maximum value of O 3 _8h_C was in summer and the minimum value was in winter (Fig. 2b). At the yearly scale, the maximum values of the annual average PM 2.5 _C, PM 10 _C, SO 2 _C, and CO_C were in 2016; the maximum value of the annual average NO 2 _C was in 2017; while the maximum value of the annual average O 3 _8h_C was in 2019 (Fig. 2c). Annual average PM 2.5 _C, PM 10 _C, SO 2 _C, NO 2 _C, and CO_C were lower in 2020 compared to all the other years, which was mainly attributed to the strict lockdown in Wuhan in early 2020.
Overall, all the air pollutants showed distinct seasonal patterns at all sites (Fig. 3) (Fig. 4). The NO 2 _C was lower while  www.nature.com/scientificreports/ O 3 _8h_C and SO 2 _C were higher in suburban sites than that in urban central sites. The PM 10 _C, SO 2 _C, NO 2 _C, and CO_C were higher in suburban and urban central sites than in the control site (Fig. 4).

The correlation between air pollutants and other factors. Correlations between the six air pollut-
ants. Significant correlations were found between the concentrations of the six air pollutants (p < 0.01), except for the relevance between O 3 _8h_C and PM 10 _C (p > 0.05) (

Correlations between air pollutants and influencing factors. The correlation between Temperature
(T) and O 3_ 8h_C was significantly positive (p < 0.01), while the correlations between the other pollutants and T were significantly negative (p < 0.01) ( Table 2). Relative air humidity (RH) was negatively correlated with SO 2 _C and O 3_ 8h_C (p < 0.05), while the correlations between the other pollutants and RH were insignificant (p > 0.05) ( Table 2). Precipitation (Prec) was negatively correlated with all pollutants (p < 0.01) except O 3_ 8h_C ( Table 2). There was no significant correlation between air pollutants and wind speed (w) (p > 0.05) ( Table 2). PM 2.5 _C, PM 10 _C, SO 2 _C, NO 2 _C, and CO_C presented a significantly negative relationship with GDP, PGDP, PRP, RA, CV, and GRB (p < 0.05 or p < 0.01). Significantly negative relationships were found between PM 2.5 _C, SO 2 _C, NO 2 _C, CO_C, and PD (p < 0.05 or p < 0.01). PM 2.5 _C displayed a significantly positive relationship with EC, CAC , CKC, and FOC. PM 10 _C displayed a significantly positive relationship with FOC. SO 2 _C displayed a significantly positive relationship with EC, CAC , CKC, COC, FOC, and EPC and NO 2 _C displayed a significantly positive relationship with CAC , CKC, and FOC. There was no correlation between CO_C and EC, CAC , CKC, COC, FOC, EPC, and between O 3 _8h_C and all the socio-economic indicators (Table 3).

Discussion
Temporal variations in air pollutants. The monthly and seasonal trends in PM 2.5 _C, PM 10   www.nature.com/scientificreports/ (Fig. 5), and fewer combustion sources. In comparison, emissions from heat sources were important contributors to the higher PM 2.5 _C, PM 10 _C, SO 2 _C, NO 2 _C, and CO_C in winter 38,39 because of the lowest temperature (Fig. 5). O 3 _8h_C showed a double-peak change pattern that mostly peaked in summer due to higher solar radiation and temperatures in summer time (Fig. 5) could contribute to the photochemistry activity and thus the formation of O 3 40,41 . The PM 2.5 _C, PM 10 _C, SO 2 _C, NO 2 _C, and CO_C decreased year by year and were lowest in 2020, mainly attributed to the effectiveness of the environmental protection and pollution control strategies 23 , and the strict www.nature.com/scientificreports/ lockdown in Wuhan in early 2020. The phenomenon of pollutant reduction during the COVID-19 lockdown in this study is consistent with that reported in previous studies 42 . The decrease in SO 2 _C in Wuhan city indicated an effective upgrading of key industrial sectors (electric power and steel, etc.), especially the ultra-low emission transformation of electric power, the elimination of small and medium-sized coal-fired boilers, the transformation of heating from coal to gas and electricity. This was consistent with the study by Li et al. 26 , which showed that the total SO 2 emission in China in 2016 was 75% less than that in 2007. The decrease in CO_C and NO 2 _C was mainly attributed to the effective regulation of traffic-related and coal combustion emissions. These all    Table 3. Correlations between the yearly concentrations of the six pollutants and socio-economic indicators during 2009-2019 (GDP gross domestic product; PGDP per capita regional GDP; PRP permanent resident population; PD population density; RA road area; CV number of civilian vehicles; PPGA per capita area of parks and green spaces; GRB green coverage rate of built-up areas; EC total energy combustion; CAC coal combustion; CKC coke combustion; COC crude oil combustion; FOC fuel oil combustion; EPC electric power combustion; **p < 0.01; *p < 0.05).  www.nature.com/scientificreports/ in the same year. The PM 2.5 _C and PM 10 _C were higher in urban and industrial sites and lower in the control site, indicating high particulate matter pollution at urban and industrial sites. Wang et al. 42 also found higher PM2.5_C and PM10_C at industrial and urban sites than at mountainous sites. The SO 2 _C were higher in industrial sites, suggesting that industrial manufacturing processes were a significant pollution source of SO 2 emission in Wuhan. The findings are consistent with Syafei et al. 10 . The NO 2 _C was lower, while O 3 _8h_C was higher in suburban sites. The finding that O 3_ 8h_C was higher in suburban sites is consistent with Wang et al. 42 , while the lower NO 2 _C in suburban areas was mainly related to the lower traffic volumes there 10 . Previous studies have also shown that automobile exhaust is the primary source of urban nitrogen oxide pollution 42 . The annual average CO_C during 2016-2020 showed no spatial differences were not consistent with Syafei et al. 10 and Wang et al. 42 . This might mainly be attributed to the consistency in the spatial distribution of pollutant emissions 45 and the impact of land use/land coverage in Wuhan city. Therefore, the extent of development and land use/land coverage should be considered further to explore spatial differences in the concentrations of air pollutants. Thus, urban green space could be integrated, and landscape patterns could be optimized to improve air quality and decrease air pollutant concentrations.

Correlations between air pollutants and influencing indicators. Significant positive correlations
between PM 2.5 _C, PM 10 _C, SO 2 _C, NO 2 _C, and CO_C suggested that they had originated from the same sources or were impacted by the same drivers 23,46 . This indicated that control measures could simultaneously decrease the concentrations of these pollutants 23 . However, the negative correlations between O 3 _8h_C and the other pollutants indicate the difficulty in controlling the six pollutants simultaneously 46,47 . Further studies should be explored to reveal the formation and control strategies of O 3 _8h in Wuhan.
Correlations between the six pollutants and the meteorological indicators suggested that the wet deposition process (e.g., scavenging and wash-out) attributed to the increase in Prec could reduce PM 2.5 _C, PM 10 _C, SO 2 _C, NO 2 _C, and CO_C 48,49 . The rise in air temperature, on the one hand, could intensify the activity of atmospheric molecules to some extent, leading to the diffusion of PM 2.5 , PM 10 , SO 2 , NO 2 , and CO. On the other hand, the rise in temperature could intensify the photochemical reaction in the atmosphere, leading to the rise of O 3 _8h_C. This also explains the monthly and seasonal pattern of PM 2.5 _C, PM 10 _C, SO 2 _C, NO 2 _C, CO_C, and O 3 _8h_C 23,50 . There was a significant negative correlation between RH and O 3 _8h_C, indicating that ozone was easy to accumulate under low humidity conditions, and ozone concentration decreased with increasing relative air humidity. Wind speed showed no significant relationship with the air pollutants in this study, showing that wind speed did not intensify air turbulence to improve air quality in Wuhan. PM 2.5 _C, PM 10 _C, SO 2 _C, NO 2 _C, and CO_C had a significantly negative relationship with GDP, PGDP, PD, PRP, RA, CV, and GRB. PM 2.5 _C, SO 2 _C, and NO 2 _C displayed a significantly positive relationship with coal and energy consumption. The increase in PPGA and GRB and the decrease in EC, CAC , CKC, COC, FOC, and EPC have compensated for the adverse effects of the increase in GDP, PGDP, PRP, PD, RA, CV in the same period, leading to an overall decrease in PM 2.5 _C, PM 10 _C, SO 2 _C, NO 2 _C, and CO_C from 2009 to 2020 in Wuhan. An increase in PPGA and GRB mainly reveals the investments in environmental protection, while a decrease in EC, CAC , CKC, COC, FOC, and EPC mainly reveals the controls on anthropogenic sources, indicating that increase in atmospheric environmental carrying capacity and effective emission-cutting measures were effective in reducing the accumulation of PM 2.5 , PM 10 , SO 2 , NO 2 , and CO. Air pollutants were inversely proportional to vehicles ownership, which was attributed to the widespread use of clean energy vehicles. Compared to the other pollutants, the correlation between O 3_ 8h_C and the socioeconomic factors suggested that the control of O 3_ 8h_C is still a challenge for Wuhan. The urban sites with the highest population densities, high numbers of automobiles, and clustered commercial living zones have relatively stable air pollutant emissions. The air pollution caused by municipal emissions has become a dominant source in urban areas. The industrial site with www.nature.com/scientificreports/ high energy consumption intensity, leading to a high concentration of heavy pollution industrial activities. The air pollutants from urban and industrial sites were also transferred to the other sites, which was a major cause of Wuhan's poor air quality.

Conclusion
This study examined the tempo-spatial characteristics of PM 2.5 _C, PM 10 _C, SO 2 _C, NO 2 _C, CO _C, and O 3 _8h_C, monitored at 22 stations in Wuhan city from January 2016 to December 2020, and their correlations with the meteorological and socio-economic factors. Based on 5-year data, we found that the maximum values of PM 2.5 _C, PM 10 _C, SO 2 _C, NO 2 _C, and CO _C were in winter, while O 3_ 8h_C were in summer. The temporal variations in air pollutants showed that annual average PM 2.5 _C, PM 10 _C, SO 2 _C, NO 2 _C, and CO _C were lower in 2020 than in other years. The spatial variations in air pollutants indicated that the PM 2.5 _C and PM 10 _C were higher in urban and industrial sites and lower in the control site. The SO 2 _C was higher in industrial sites. The NO 2 _C were lower, and O 3_ 8h_C was higher in suburban sites, while CO showed no spatial differences in their concentrations. Significant correlations were found between the concentrations of the six air pollutants, except for the relevance between O 3 _8h_C and PM 10 _C. The correlations between O 3_ 8h_C and the other pollutants were more complex than those between the other 5 pollutants. PM 2.5 _C, PM 10 _C, NO 2 _C, and CO _C were significantly negatively correlated with temperature and precipitation but insignificantly associated with relative air humidity. SO 2 _C was significantly negatively associated with temperature, relative air humidity, and precipitation. O 3_ 8h_C was significantly positively associated with temperature while significantly negatively associated with relative air humidity. There was no significant correlation between air pollutants and wind speed. Several socioeconomic factors drove air quality concentrations. The urban and industrial sites with high population densities, number of automobiles, and energy consumption intensity, led to a high concentration of pollutants. An increase in atmospheric environmental carrying capacity and effective emission-cutting measures effectively reduced the accumulation of PM 2.5 , PM 10 , SO 2 , NO 2 , and CO. Overall, this study provided scientific insights that tempospatial characteristics and major influencing factors should be taken into account to improve Wuhan's air quality.

Data availability
All data analyzed during this study are included in this published article, and could be obtained upon request from B.J. (email: jbshuibao415@126.com).